The present invention relates to a running control system for a mobile robot used in robots which move in an environment which changes, such as department stores and offices and the like, and more particularly, relates to a running control system using a multiple sensor information integration system which effectively fuses information of multiple sensors and removes systematic errors of each non-contact sensor. Moreover, in this specification, the terms "navigation system" and "running control system" are used with the same intended meaning.
Conventionally, a method of moving whereby a path of motion has been set by guidance lines and landmarks, and this is detected by a robot has been practically used for unmanned conveyor vehicles in factories and the like.
Marks and the like are installed on the floor surface or guidance lines are used as the navigation system used in such mobile robots, and steering control is performed by measuring the environment and referring to map data to calculate the present position, generate the running path and guide the mobile robot to an objective position. Alternatively, the ends of a path or corridor can be followed in image, in a steering control system whereby the mobile robot runs along a path or corridor.
However, with these conventional navigation system, it is necessary to have the prior installation within the environment of facilities when guidance lines and marks are used and so in the case where it is necessary to change the moving path, there has been the problem that this requires much trouble and expense to reinstall the marks and guidance lines. In addition, the measurement of environments using stereo vision, rangefinders, ultrasonic wave sensors and the like and the referral to map data to calculate the present position requires time for the measurement of the environment and so there are practical problems as far as application to navigation is concerned, and not only this, it is necessary to measure the environment to a high degree of precision in order to generate an accurate path for the robot. This poses problems for real-time running and places restrictions upon the environment in which the robot can run. Not only this, when there is navigation along a path or corridor end, it is only possible to have application in places where there are path ends or corridor ends and so there is the problem that there are added restrictions upon the running conditions.
Still furthermore, this method is effective in stationary environments in which the paths of motion are fixed, but in a general environment where there are moving objects such as persons and the like as is the case for banks, department stores, offices and the like, it is necessary to change the path of motion in accordance with the temporary positions of persons and so motion control cannot be performed quickly in environments such as these.
There has been much research into control methods which can correspond to such dynamic environments.
The level of this research is such that there is a linear method consisting of measurement.fwdarw.creation of environment notation.fwdarw.motion plan creation, and with this method, robot control is performed by functions for the creation of the map of the motion environment and the creation of the motion plan, there is the detection of positions of landmarks given beforehand, the determination of the present position by referral to the maps, and tracking after the optimum path has been inferred.
However, when this linear type of control method is used, there are many cases where it is easy for changes in the environmental conditions such as the illumination change in addition to changes in the conditions of the dynamic environment, and when there is a failure of the intermediate processing due to a drop in the reliability of measurement, it is not possible to create the plan for the running of the robot and so there is a large loss in the integrity of the most important elements for mobile robots. In addition, the extraction of landmarks, the increase of the referral cost, large increases in the cost of creating the environmental maps and the like cause extremely difficult problems for the creation of the optimum path on the basis of the measurement results, and it is difficult to apply this to real-time running.
On the other hand, in recent years, there has been proposed a subsumption architecture in which behavior levels such as obstacle avoidance, patrol, landmark search and the like are stratified and operated in parallel and when there has been a failure on the higher order level, there is transfer of the operating level to a lower level. Much research has been performed into this method. According to this method, there is prior stratifying into motion behavior units and so from measurement until the determination of movement is performed within the one step, and so there is the advantage of being able to perform safe motion control without the high-performance motion plan creation functions described above.
However, since the steps are fixed and are independent, there is no mechanism for positively solving the problem of failure of a certain motion. When this occurs, it is difficult for the motion of higher-order levels to be guided and it becomes difficult for the mobile robot to fulfill its duties. For example, if the case for when a mobile robot at a certain point is to be moved to an end objective, reflexive behavior such as obstacle avoidance is usually placed on the lower order levels and moving on an intelligent levels such as running to an objective position is ranked in one of the higher order levels. Accordingly, if the mobile robot loses sight of the objective, and the intermediate processing of the higher order level is lost, then there is shift to obstacle avoidance behaviors which is of a lower level. In dynamic environments there is a large possibility that the objective will be lost sight of and in these cases, there is a high possibility that only the lower order (reflexive levels) behavior levels such as obstacle avoidance will be selected and so it is difficult for higher level (intelligent level) behaviors to be guided.
It has not yet been possible to realize a system that can effectively perform intelligent behavior such as tracking and search of objective points using only reflexive action such as obstacle avoidance in dynamic environments as has been described above.
In complex dynamic environments, one important technology for the realization of running control systems such as status monitoring apparatus and autonomous systems and the like, is a technology which fuses multiple sensor information so as to improve the robustness and degree of autonomy.
Conventionally, there have been proposed various types of technologies for unifying multiple sensors and based on this probability theory research. In this probability theory research, a model of random errors is made by probability analysis. Then, a plural number of probability analyses are unified into a single probability analysis by one of many methods such as the Baysian research method, the Dempster-Shafer Theory, fuzzy set theory or simple probability multiplication and addition method. The probability method is fairly well established and is applicable to removing random errors. These random errors can compensate for prior characteristics due to careful correction procedures. Accordingly, if a random error of a sensor is known then those results can be used for the processing of that sensor information.
However, systematic error is determined by the random error as well as by incompleteness of sensor functions.
Many mobile robot systems use acoustic sensors. The reason for this is that they are an inexpensive means of judging the proximity of an object item through measurement of the time for the transmission of an echo.
However, these acoustic sensors have a problem in the interpretation of the signals which are returned by the sensor because of specular reflections and corner reflections and because of this, many researchers have introduced ancillary sensor systems and systems which use only sound have fallen into disuse. These systems are called sensory systems and use probability research methods (such as in L. Matthies and A. Elfes, IEEE Int. Conf. Robotics and Automation, 1988, 727-733.) In recent years, the avoidance of obstacles and navigation using only sound is using both specular reflections and corner reflections. However, such systems have an extremely limited navigational performance for wall tracking and obstacle avoidance. Furthermore, such systems are not applicable to unspecified dynamic environments.
There are various types of mobile robot systems which use active laser scanners. An active laser scanner transmits laser signals generated by a laser diode which is driven in a near infrared band and so measure the distance from the transmission time of infrared light reflected from an object. This type of active laser scanner also has its own characteristic problems. More specifically, the strength of the laser beam which is reflected is dependent upon the color, material and the surface status of the surface of the object of scanning. For example, with an artificial laser scanner, it is not possible to provide an effective bandwidth with respect to objects which are black. Many researchers have had limited success with the use of various discovery algorithms and attempts are being made to remove systematic errors but this still involves much difficulty.
Measures with respect to systematic errors of conventional non-contact sensor information are still largely lacking.